Data-Driven-Based Aggregate ACLs External Modeling and Load Tracking Control Strategy

Air conditioning loads (ACLs) can be used as flexible loads to participate in load shaving and accommodate the fluctuation caused by renewable energies. However, most of the existing control methods do not consider time-varying characteristic of the aggregate ACLs' external model (AAEM). This paper proposes a data-driven AAEM and a corresponding load tracking control strategy. Based on the cost function of individual ACLs considering thermodynamic characteristics, the data-driven AAEM is established by using neural network. By this way, computational complexity of calculation AAEM is reduced. The load tracking control strategy considering the data-driven AAEM can effectively reduce the number of times of switch and the total cost. The simulation results verify the effectiveness of this method.